Chen Xia, Sun Zhan-Li, Zhang Ying
School of Information and Computer, Anhui Agricultural University, Hefei, China.
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institute of Physical Science and Information Technology, Anhui University, Hefei, China.
Front Neurosci. 2023 May 19;17:1191574. doi: 10.3389/fnins.2023.1191574. eCollection 2023.
In this study, a multiple-constraint estimation algorithm is presented to estimate the 3D shape of a 2D image sequence. Given the training data, a sparse representation model with an elastic net, i.e., -norm and -norm constraints, is devised to extract the shape bases. In the sparse model, the -norm and -norm constraints are enforced to regulate the sparsity and scale of coefficients, respectively. After obtaining the shape bases, a penalized least-square model is formulated to estimate 3D shape and motion, by considering the orthogonal constraint of the transformation matrix, and the similarity constraint between the 2D observations and the shape bases. Moreover, an Augmented Lagrange Multipliers (ALM) iterative algorithm is adopted to solve the optimization of the proposed approach. Experimental results on the well-known CMU image sequences demonstrate the effectiveness and feasibility of the proposed model.
在本研究中，提出了一种多约束估计算法来估计二维图像序列的三维形状。给定训练数据，设计了一种具有弹性网络（即 -范数和 -范数约束）的稀疏表示模型来提取形状基。在稀疏模型中，分别施加 -范数和 -范数约束来调节系数的稀疏性和尺度。获得形状基后，通过考虑变换矩阵的正交约束以及二维观测值与形状基之间的相似性约束，构建一个惩罚最小二乘模型来估计三维形状和运动。此外，采用增广拉格朗日乘子（ALM）迭代算法来求解所提方法的优化问题。在著名的CMU图像序列上的实验结果证明了所提模型的有效性和可行性。
